Internal Business Processes Perspective

Row

Total Number of Trainings

640

Total Number of New features

260

Trainings Per New Feature

Row

Employees’ Trainings

Innovation and Learning Perspective

Row

Total time taken

61

Average Response Time

Mode of the Response Time

###Total Time taken

Row

System Incidents

Customer’s Perspective

Row

Average NPS

2019 NPS

2020 NPS

2021 NPS

2022 NPS

Row

NPS by Year

Number of Surveys

Financial Perspective

Row

Total Investment Aount

$200M

Total Approved Investment Amount

$25M

Average Expected Roi

Row

Total Invesment Amount by Proposal Type

Total Invesment Amount by Year

Investment Amount by Status

Row

Expected ROI by Proposal Type

Expected ROI by Year

Expected ROI by Status

International business occurs in many different formats: The movement of goods from country to another (exporting, importing, trade) Contractual agreements that allow foreign firms to use products, services, and processes from other nations (licensing, franchising)

---
title: "Barcorde Based Student Record System"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: [ "twitter", "facebook", "menu"]
    source_code: embed
---


```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(ggplot2)
library(rpivotTable)
library(plotly)
library(dplyr)
library(highcharter)
library(openintro)
library(scales)
```
```{r}
roi<-read.csv("roi.csv")
nps<-read.csv("nps.csv")
survey<-read.csv("surveys.csv")
Si<-read.csv("system_incidents.csv")
nf<-read.csv("new_features.csv")
ip<-read.csv("investment_proposals.csv")
et<-read.csv("employee_training.csv")
av<-read.csv("Average_response_time.csv")
```
```{r}
mycolors<-c("grey","blue","maroon","#D4AF37")
```

Internal Business Processes Perspective
===========================================
Row
---------------------------------------
### Total Number of Trainings
```{r}
valueBox(sum(et$Employee.Training),
         icon = "fa-bicycle")
```

### Total Number of New features
```{r}
valueBox(sum(nf$New.Features),
         icon = "fa-bicycle")
```

### Trainings Per New Feature
```{r}
Traingings_per_new_feature=sum(et$Employee.Training)/sum(nf$New.Features)
gauge(round(Traingings_per_new_feature,
            digits = 2),
            min = 0,
            max = 3,
            gaugeSectors(success = c(2, 3),
                         warning = c(0.5,1 ),
                         danger = c(0, 0.5),
                         colors = c("green", "yellow", "red")))
```

Row
---------------------------------------

```{r}
p2 <- et %>%
         group_by(et$Year.2020) %>%
         summarise(et$Employee.Training) %>%
         plot_ly(labels = ~et$Year.2020,
                 values = ~et$Employee.Training,
                 marker = list(colors = mycolors)) %>%
         add_pie(hole = 0.5) %>%
         layout(xaxis = list(zeroline = F,
                             showline = F,
                             showticklabels = F,
                             showgrid = F),
                yaxis = list(zeroline = F,
                             showline = F,
                             showticklabels=F,
                            showgrid=F))
p2 
```

### Employees' Trainings 

```{r}
p1 <- nf %>%
         group_by(nf$Year) %>%
         summarise(sum(nf$New.Features)) %>%
         plot_ly(x = ~nf$Year,
                 y = ~nf$New.Features,
                 type = 'bar',
                 marker = list(color = 'blue')) %>%
         layout(xaxis = list(title = "Year"),
                yaxis = list(title = 'Number of Trainings'))
p1
```

### 
```{r}
p1 <- et %>%
         group_by(et$Year.2020) %>%
         summarise(sum(et$Employee.Training)) %>%
         plot_ly(x = ~et$Employee.Training,
                 y = ~et$Year.2020,
                 type = 'bar',
                 marker = list(color = 'blue')) %>%
         layout(xaxis = list(title = "Year"),
                yaxis = list(title = 'Number of Trainings'))
p1
```

Innovation and Learning Perspective
===========================================
Row 
--------------------------------------
### Total time taken
```{r}
valueBox(sum(av$Time.taken.to.respond..sec.),
         icon = "fa-bicycle")
```

### Average Response Time 

```{r}
gauge(round(5.5,
            digits = 2),
            min = 0,
            max = 10,
            gaugeSectors(success = c(7, 10),
                         warning = c(5.,8 ),
                         danger = c(0, 4),
                         colors = c("green", "yellow", "red")))
```

### Mode of the Response Time

```{r}
gauge(round(12,
            digits = 2),
            min = 0,
            max = 10,
            gaugeSectors(success = c(12, 15),
                         warning = c(8,11 ),
                         danger = c(0, 7),
                         colors = c("green", "yellow", "red")))
```

###Total Time taken

```{r}

```


Row 
--------------------------------------
### System Incidents 
```{r}
p3 <- Si %>%
 group_by(Si$Year.2022) %>%
         summarise(sum(Si$System.uptime.incidents)) %>%
         plot_ly(x = ~Si$Year.2022,
                 y = ~Si$System.uptime.incidents,
                 type = 'bar',
                 marker = list(color = '#D4AF37')) %>%
         layout(xaxis = list(title = "2022"),
                yaxis = list(title = 'Number of system uptime incidents'))
p3
```


Customer's Perspective
===========================================
Row
------------------------------------------------------
### Average NPS 
```{r}
gauge(round(mean(nps$Net.Promoter),
            digits = 2),
            min = -100,
            max = 100,
            gaugeSectors(success = c(50, 100),
                         warning = c(0,50 ),
                         danger = c(-100, 0),
                         colors = c("green", "yellow", "red")))
```

### 2019 NPS 
```{r}
gauge(round(85,
            digits = 2),
            min = -100,
            max = 100,
            gaugeSectors(success = c(60, 100),
                         warning = c(0,60 ),
                         danger = c(-100, 0),
                         colors = c("green", "yellow", "red")))
```

### 2020 NPS 
```{r}
gauge(round(60,
            digits = 2),
            min = -100,
            max = 100,
            gaugeSectors(success = c(70, 100),
                         warning = c(0,70 ),
                         danger = c(-100, 0),
                         colors = c("green", "yellow", "red")))
```
### 2021 NPS 
```{r}
gauge(round(75,
            digits = 2),
            min = -100,
            max = 100,
            gaugeSectors(success = c(60, 100),
                         warning = c(0,60 ),
                         danger = c(-100, 0),
                         colors = c("green", "yellow", "red")))
```
### 2022 NPS 
```{r}
gauge(round(89,
            digits = 2),
            min = -100,
            max = 100,
            gaugeSectors(success = c(60, 100),
                         warning = c(0,60 ),
                         danger = c(-100, 0),
                         colors = c("green", "yellow", "red")))
```
Row
--------------------------------------------------
### NPS by Year
```{r}
p3 <- nps %>%
 group_by(nps$Year) %>%
         summarise(sum(nps$Net.Promoter)) %>%
         plot_ly(x = ~nps$Year,
                 y = ~nps$Net.Promoter,
                 type = 'bar',
                 marker = list(color = '#D4AF37')) %>%
         layout(xaxis = list(title = "2022"),
                yaxis = list(title = 'Net promoter Score'))
p3
```
### Number of Surveys 
```{r}
p3 <- survey %>%
 group_by(survey$Year) %>%
         summarise(sum(survey$Student.Surveys)) %>%
         plot_ly(x = ~survey$Year,
                 y = ~survey$Student.Surveys,
                 type = 'bar',
                 marker = list(color = 'blue')) %>%
         layout(xaxis = list(title = "2022"),
                yaxis = list(title = 'Number of Survey '))
p3
```

Financial Perspective
===========================================
Row
----------------------------------------------
### Total Investment Aount
```{r}
MillionForm <- dollar_format(prefix = "$",suffix="M")
valueBox(MillionForm((sum(ip$Investment.Amount..million.))),
        icon = "fa-money",
        color='white')
```
### Total Approved Investment Amount
```{r}
MillionForm <- dollar_format(prefix = "$",suffix="M")
valueBox(MillionForm(25),
        icon = "fa-money",
        color='white')
```
### Average Expected  Roi
```{r}
gauge((scales::percent(mean(ip$Expected.ROI....)/100)),
            min = 0,
            max = 50,
            gaugeSectors(success = c(25, 50),
                         warning = c(24, 15),
                         danger = c(0, 14),
                         colors = c("green", "darkorange", "red")))
```
Row
-----------------------------------------------
### Total Invesment Amount by Proposal Type
```{r}
p4 <- ip %>%
  filter(!is.na(ip$Proposal.Name)) %>%
  group_by(ip$Proposal.Name) %>%
  plot_ly(labels = ~ip$Proposal.Name,
          values = ~ip$Investment.Amount..million.,
          marker = list(colors = mycolors)) %>%
  add_pie(hole = 0.5) %>%
  layout(xaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid = F),
         yaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid = F))
p4

```

### Total Invesment Amount by Year
```{r}
p3 <- ip %>%
 group_by(ip$Year) %>%
         summarise(sum(ip$Investment.Amount..million.)) %>%
         plot_ly(x = ~ip$Year,
                 y = ~ip$Investment.Amount..million.,
                 type = 'bar',
                 marker = list(color = 'blue')) %>%
         layout(xaxis = list(title = "Year"),
                yaxis = list(title = 'Investment Amount '))
p3
```
### Investment Amount by Status
```{r}
p4 <- ip %>%
  filter(!is.na(ip$Proposed.Status)) %>%
  group_by(ip$Proposed.Status) %>%
  plot_ly(labels = ~ip$Proposed.Status,
          values = ~ip$Investment.Amount..million.,
          marker = list(colors = mycolors)) %>%
  add_pie(hole = 0.5) %>%
  layout(xaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid = F),
         yaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid = F))
p4

```
Row
-----------------------------------------------
### Expected ROI by Proposal Type
```{r}
p4 <- ip %>%
  filter(!is.na(ip$Proposal.Name)) %>%
  group_by(ip$Proposal.Name) %>%
  plot_ly(labels = ~ip$Proposal.Name,
          values = ~ip$Expected.ROI....,
          marker = list(colors = mycolors)) %>%
  add_pie(hole = 0.5) %>%
  layout(xaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid = F),
         yaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid = F))
p4

```

### Expected ROI by Year
```{r}
p3 <- ip %>%
 group_by(ip$Year) %>%
         summarise(sum(ip$Expected.ROI....)) %>%
         plot_ly(x = ~ip$Year,
                 y = ~ip$Expected.ROI....,
                 type = 'bar',
                 marker = list(color = 'blue')) %>%
         layout(xaxis = list(title = "Year"),
                yaxis = list(title = 'Expected ROI in % '))
p3
```
### Expected ROI by Status
```{r}
p4 <- ip %>%
  filter(!is.na(ip$Proposed.Status)) %>%
  group_by(ip$Proposed.Status) %>%
  plot_ly(labels = ~ip$Proposed.Status,
          values = ~ip$Expected.ROI....,
          marker = list(colors = mycolors)) %>%
  add_pie(hole = 0.5) %>%
  layout(xaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid = F),
         yaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid = F))
p4

```

# International business occurs in many different formats: The movement of goods from country to another (exporting, importing, trade) Contractual agreements that allow foreign firms to use products, services, and processes from other nations (licensing, franchising)